Regional non-stationary future extreme rainfall under changing climate over Asian Monsoon Region

Sojan, Jency M.; Srivastav, Roshan; Meghana, N.

doi:10.1016/j.atmosres.2022.106592

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…The interpolation from coarse resolution to high resolution may introduce some bias. However, the bias introduced by this interpolation is much smaller than that of the GCM‐simulated precipitation (Nahar et al, 2017; Sojan et al, 2023), especially when applying a bias correction, the slight bias introduced by interpolation can be ignored. Since CMIP5 and CMIP6 used different emission scenarios, we use RCP2.6, RCP4.5 and RCP8.5 as uniform names for low‐, medium‐ and high‐emission scenarios when describing future daily precipitation extremes.…”

Section: Methodsmentioning

confidence: 99%

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

Liu,

Chen,

Xiong

et al. 2024

Intl Journal of Climatology

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The rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario.

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Section: Methodsmentioning

confidence: 99%

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

Liu,

Chen,

Xiong

et al. 2024

Intl Journal of Climatology

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“…This test indicator, which is the supreme transformation between theoretical and data-based distributions, serves as a quantity of in what manner the hypothetical spreading fits this data .In essence, it quantifies the level of agreement between a data-based F (x) and a theoretical based F 0 (x). The goodness-of-fit calculates the maximum difference between F (x) and F 0 (x), denoted as L in (1). If the difference is significant, it indicates a disparity between the observed data with the mathematical simulations.…”

Section: K-s Testmentioning

confidence: 99%

“…Annual maximum rainfall (AMR) categorizes into extreme rainfall regions, crafting future intensity-duration-frequency (IDF) curves. Envisioned: Framework cuts climate model bias, estimates reliable AMR rainfall by Sojan et al [1]. The study aims to assess rainfall erosivity changes in Brazil's Tocantins-Araguaia basin amid future climate conditions.…”

Section: Introductionmentioning

confidence: 99%

Probability distributions in Kerala’s rainfall: implications for hydro energy planning

Baranitharan,

Chandran,

Subramaniyan Mathan

et al. 2024

IJECE

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Heavy rainfall has consistently acted as the primary catalyst for floods, resulting in numerous casualties and significant economic losses globally. Rainfall forecasting is accomplished by analysing existing rainfall data, which is then used to analyse the hydraulic system’s features. Gaining an understanding of rainfall requirements is a crucial challenge for every location, particularly in the case of India, given its diverse geographical area, population, and other influencing factors that impact various demands. This study evaluated the rainfall data for a span of 1990-2021 in six districts of Kerala State, India. To match the rainfall data from all districts, we utilized both Kaumarasamy-distribution and Dagum-distributions. Various Probabilistic tests, were employed to comparing these distributions. The results revealed that, in Kasargod, the Kumarasamy distribution demonstrates superior goodness-of-fit with the lowest Kolmogorov-Smirnov statistic (0.0597) and Anderson-darling statistic (2.271). However, in Wayanad, Malappuram, Palakkad, Idukki, and Trivandrum, the Dagum distribution consistently exhibits the most accurate fit, evident from its lowest Kolmogorov-Smirnov statistics (0.07447, 0.05435, 0.0556, 0.03636, 0.04291) and favourable Chi-Squared statistics (19.471, 8.4907, 19.239, 5.7318, 7.5297). These results emphasize the regional variation in precipitation data and the suitability of specific distribution models for accurate representation across differentlocations.

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“…The global climate models (GCMs) used in the fifth and sixth phase of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively) [15,16] have been widely used to study current and future changes in global precipitation extremes [9,11,17]. They have also been adapted on regional scales in Africa [18,19], Asia [14,[20][21][22][23], Europe [24], North America [25] and South America [26,27].…”

Section: Introductionmentioning

confidence: 99%

Predicting Changes in Population Exposure to Precipitation Extremes over Beijing–Tianjin–Hebei Urban Agglomeration with Regional Climate Model RegCM4 on a Convection-Permitting Scale

et al. 2023

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In this study, we have investigated changes in precipitation extremes and the population’s exposure to these extremes during 2091–2099 in China’s Beijing–Tianjin–Hebei (JJJ) region relative to the historical period of 1991–1999. First, the regional climate model RegCM4, with a hydrostatic dynamic core, was run for east Asia, including China, at a 12 km resolution for 1990–1999 and 2090–2099. This model is forced by global climate model (GCM) MPI-ESM1.2-HR under the middle shared socioeconomic pathways (SSP245). The first year was used as a model spinup. Then, the 12 km results were used to force RegCM4 with a non-hydrostatic dynamic core (RegcM4-NH) at a 3 km convection-permitting scale over the JJJ region during the historical and future periods. Future precipitation extremes were predicted to increase over the whole of China and its four subregions, while decreases were predicted over the JJJ region. This may partly be caused by lower increases in specific humidity over the JJJ region. The percentage contributions of the three components of total population exposure, i.e., changes in exposure due to changes in the population, precipitation extremes and the joint impact of the population and extremes, were then analyzed. Changes in the population and wet extremes were closely related to changes in the total exposure over the JJJ region. The population is the dominant factor that most impacts the total exposure to dry extremes. Finally, changes in future population exposure to precipitation extremes per degree of warming were quantified for the JJJ region.

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Regional non-stationary future extreme rainfall under changing climate over Asian Monsoon Region

Cited by 4 publications

References 35 publications

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

Probability distributions in Kerala’s rainfall: implications for hydro energy planning

Predicting Changes in Population Exposure to Precipitation Extremes over Beijing–Tianjin–Hebei Urban Agglomeration with Regional Climate Model RegCM4 on a Convection-Permitting Scale

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